{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 Physical GPUs, 2 Logical GPUs\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import graphgallery \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# Set if memory growth should be enabled for ALL `PhysicalDevice`.\n",
    "graphgallery.set_memory_growth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.1.2'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.4.0'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graphgallery.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the Datasets\n",
    "+ cora\n",
    "+ citeseer\n",
    "+ pubmed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from graphgallery.data import Planetoid\n",
    "\n",
    "# set `verbose=False` to avoid these printed tables\n",
    "data = Planetoid('cora', root=\"~/GraphData/datasets\", verbose=False)\n",
    "graph = data.graph\n",
    "idx_train, idx_val, idx_test = data.split()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'citeseer', 'cora', 'pubmed'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.supported_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training...\n",
      "200/200 [==============================] - 8s 41ms/step - loss: 3.6291 - acc: 0.9357 - val_loss: 3.2890 - val_acc: 0.8180 - time: 8.2123\n",
      "Testing...\n",
      "1/1 [==============================] - 0s 337ms/step - test_loss: 3.5493 - test_acc: 0.8330 - time: 0.3368\n",
      "Test loss 3.5493, Test accuracy 83.30%\n"
     ]
    }
   ],
   "source": [
    "from graphgallery.nn.models import OBVAT\n",
    "model = OBVAT(graph, attr_transform=\"normalize_attr\", device='GPU', seed=123)\n",
    "model.build()\n",
    "# train with validation\n",
    "his = model.train(idx_train, idx_val, verbose=1, epochs=200)\n",
    "# train without validation\n",
    "# his = model.train(idx_train, verbose=1, epochs=100)\n",
    "loss, accuracy = model.test(idx_test)\n",
    "print(f'Test loss {loss:.5}, Test accuracy {accuracy:.2%}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show model summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "attr_matrix (InputLayer)        [(None, 1433)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               multiple             0           attr_matrix[0][0]                \n",
      "                                                                 graph_convolution[0][0]          \n",
      "                                                                 tf_op_layer_add[0][0]            \n",
      "                                                                 graph_convolution[1][0]          \n",
      "__________________________________________________________________________________________________\n",
      "adj_matrix (InputLayer)         [(None, None)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "graph_convolution (GraphConvolu (None, 16)           22928       dropout[0][0]                    \n",
      "                                                                 adj_matrix[0][0]                 \n",
      "                                                                 dropout[2][0]                    \n",
      "                                                                 adj_matrix[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "graph_convolution_1 (GraphConvo (None, 7)            112         dropout[1][0]                    \n",
      "                                                                 adj_matrix[0][0]                 \n",
      "                                                                 dropout[3][0]                    \n",
      "                                                                 adj_matrix[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "node_index (InputLayer)         [(None,)]            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "gather (Gather)                 (None, 7)            0           graph_convolution_1[0][0]        \n",
      "                                                                 node_index[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_add (TensorFlowOpLa [(2708, 1433)]       0           attr_matrix[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_StopGradient (Tenso [(None, 7)]          0           graph_convolution_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Softmax_1 (TensorFl [(None, 7)]          0           tf_op_layer_StopGradient[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_LogSoftmax_1 (Tenso [(None, 7)]          0           graph_convolution_1[1][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Softmax (TensorFlow [(None, 7)]          0           graph_convolution_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_LogSoftmax (TensorF [(None, 7)]          0           graph_convolution_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_mul_1 (TensorFlowOp [(None, 7)]          0           tf_op_layer_Softmax_1[0][0]      \n",
      "                                                                 tf_op_layer_LogSoftmax_1[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_mul (TensorFlowOpLa [(None, 7)]          0           tf_op_layer_Softmax[0][0]        \n",
      "                                                                 tf_op_layer_LogSoftmax[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Sum_1 (TensorFlowOp [(None,)]            0           tf_op_layer_mul_1[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Sum (TensorFlowOpLa [(None,)]            0           tf_op_layer_mul[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Mean_1 (TensorFlowO [()]                 0           tf_op_layer_Sum_1[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Mean (TensorFlowOpL [()]                 0           tf_op_layer_Sum[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Neg_1 (TensorFlowOp [()]                 0           tf_op_layer_Mean_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Neg (TensorFlowOpLa [()]                 0           tf_op_layer_Mean[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_mul_2 (TensorFlowOp [()]                 0           tf_op_layer_Neg_1[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_mul_3 (TensorFlowOp [()]                 0           tf_op_layer_Neg[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_add_1 (TensorFlowOp [()]                 0           tf_op_layer_mul_2[0][0]          \n",
      "                                                                 tf_op_layer_mul_3[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "add_loss (AddLoss)              ()                   0           tf_op_layer_add_1[0][0]          \n",
      "==================================================================================================\n",
      "Total params: 23,040\n",
      "Trainable params: 23,040\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "with plt.style.context(['science', 'no-latex']):\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
    "    axes[0].plot(his.history['acc'], label='Train accuracy')\n",
    "    axes[0].plot(his.history['val_acc'], label='Val accuracy')\n",
    "    axes[0].legend()\n",
    "    axes[0].set_title('Accuracy')\n",
    "    axes[0].set_xlabel('Epochs')\n",
    "    axes[0].set_ylabel('Accuracy')\n",
    "\n",
    "\n",
    "    axes[1].plot(his.history['loss'], label='Training loss')\n",
    "    axes[1].plot(his.history['val_loss'], label='Validation loss')\n",
    "    axes[1].legend()\n",
    "    axes[1].set_title('Loss')\n",
    "    axes[1].set_xlabel('Epochs')\n",
    "    axes[1].set_ylabel('Loss')\n",
    "    \n",
    "    plt.autoscale(tight=True)\n",
    "    plt.show()    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
